Ascertain IT budget outlook for 2023 overall and for specific technologies.
Determine key business and technology priorities driving 2023 spending plans.
Establish a profile of digitally mature companies in terms of the technologies and processes underlying their IT strategies.
Monitor YoY trends with respect to technology adoption and changing customer preferences.
At Big Data London, data quality and intelligence took center stage as companies strive for fast and efficient delivery of quality information — and the vendors to make it happen. Big Data London is the U.K.’s leading data and analytics event, with more than 150 vendors, 300 expert speakers, and an estimated 10,000 attendees coming together to discuss data-driven strategies. During the event, I interviewed seven vendors to get their perspectives and insights on where the market was going, the challenges they saw, and more.
Data clean rooms are moving into the mainstream as privacy concerns complicate measuring ad ROI, but the “people problem” poses challenges to data operation success. Data clean rooms are becoming the preeminent tool for brands to better understand the place of digital marketing within the broader marketing ecosystem. In fact, data clean rooms are key for measuring advertising effectiveness, especially as advertisers are relying on cookies less, due to privacy regulations and big tech changes.
More than ever, increases in data-centric developer reliance, data sources, and users push developers to understand IT purchasing to provide more value to data. The days of creating data-generating systems for a single operational or analytics application are over. Because of this, developers must become more data-centric to increase business agility and empower data consumers — including internal teams, partners, and customers. Data-centric developers are expanding their sphere of influence on IT purchasing decisions and maximizing the value of data.
Determine key challenges organizations face deploying and leveraging data protection mechanisms for their SaaS environments. Understand the education and best practices gaps that exist in protecting SaaS environments, with specific focus on key capabilities and data protection SLAs. Gain insights into key trends and requirements for successful high availability and data protection strategies for SaaS environments. Gauge end-users’ data protection knowledge and challenges for specific SaaS environments.
Many organizations use open source analytics and data management technology, which often serves as a foundation of their data-centric technology ecosystems. But deploying and managing open source tools and solutions are a challenge in many cases—a situation that calls for managed services to help ease the challenges and pave the way for increased open source adoption, especially in distributed cloud environments.
Many decision makers are interested in standardizing their analytics initiatives on a single cloud provider’s infrastructure and services. A large majority think their organization would be open to considering such a move. This research highlights what they’d be looking for by doing so—easier management, simplified support, and more. But it also points to a fundamental question: Can one vendor meet all of an organization’s data needs?
Data integration is proving to be more complex in the cloud for a majority of organizations, especially ones that use public cloud services in multi-cloud environments as part of their analytics initiatives. Many are turning to new tools and managed services to help them cope with integration challenges. In a lot of cases, though, they’re also moving to decrease the number of vendors they currently use.
Most organizations use technologies from a variety of public cloud and third-party software providers to support their data management and analytics strategy. That complicates ongoing operational management of cloud analytics environments, and multi-cloud deployments exacerbate the challenges—and the headaches they cause. This research highlights what organizations are doing to try to ease the pain without disrupting critical data workflows.
While most IT decision makers believe their organization is doing a good job of acting on data insights, it often takes weeks or months to generate and then act on those insights. There’s an opportunity to do better by deploying new technologies and making the data lifecycle more efficient—and organizations that don’t address the time-to-value gap may find themselves lagging behind faster and more agile rivals.
IT suppliers’ environmental, social, and governance (ESG) program status is a new and important evaluation factor for buyers.
See data behind this emerging trend with this free Enterprise Strategy Group Infographic, The Role of ESG Programs in IT Decision Making.
Measure the impact of environmental, social, and governance (ESG) initiatives on the IT evaluation and purchase process. Identify the groups within organizations most responsible for adhering to ESG requirements as part of IT purchases. Highlight the costs and benefits that organizations have experienced as a result of complying with ESG initiatives. Determine which vendors/brands are perceived as strong in terms of ESG and which are viewed as laggards.